title: Single channel blind source separation based local mean decomposition for biomedical applications.
abstract: Single Channel Blind Source Separation (SCBSS) is an extreme case of underdetermined (more sources and fewer sensors) Blind Source Separation (BSS) problem. In this paper, we propose a novel technique using Local Mean Decomposition (LMD) and Independent Component Analysis (ICA) combined with single channel BSS (LMD_ICA). First, the LMD was used to decompose the single channel source into a series of data sequences, which are called as Product Functions (PF), then, ICA algorithm was used to process PFs to get similar independent components and extract the original signals. A comparison was made between LMD_ICA and previously proposed single channel ICA method (EEMD_ICA). The real time experimental results demonstrated the advantage of the proposed single channel source separation method for artifact removal and in biomedical source separation applications.
authors.forename: Yina
authors.initials: Y
authors.lastname: Guo
authors.forename: Ganesh R
authors.initials: GR
authors.lastname: Naik
authors.forename: Hung
authors.initials: H
authors.lastname: Nguyen
mesh_terms: D000465:Algorithms; D004562:Electrocardiography / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted